Two are better than one: Adaptive sparse system identification using affine combination of two sparse adaptive filters

Guan Gui, Shinya Kumagai, Abolfazl Mehbodniya, Fumiyuki Adachi

Research output: Contribution to journalConference article

8 Citations (Scopus)

Abstract

Sparse system identification problems often exist in many applications, such as echo interference cancellation, sparse channel estimation, and adaptive beamforming. One of popular adaptive sparse system identification (ASSI) methods is adopting only one sparse least mean square (LMS) filter. However, the adoption of only one sparse LMS filter cannot simultaneously achieve fast convergence speed and small steady-state mean state deviation (MSD). Unlike the conventional method, we propose an improved ASSI method using affine combination of two sparse LMS filters to simultaneously achieving fast convergence and low steady-state MSD. First, problem formulation and standard affine combination of LMS filters are introduced. Then an approximate optimum affine combiner is adopted for the proposed filter according to stochastic gradient search method. Later, to verify the proposed filter for ASSI, computer simulations are provided to confirm effectiveness of the proposed filter which can achieve better estimation performance than the conventional one and standard affine combination of LMS filters.

Original languageEnglish
Article number7023132
JournalIEEE Vehicular Technology Conference
Volume2015-January
Issue numberJanuary
DOIs
Publication statusPublished - 2014 Jan 1
Event2014 79th IEEE Vehicular Technology Conference, VTC 2014-Spring - Seoul, Korea, Republic of
Duration: 2014 May 182014 May 21

ASJC Scopus subject areas

  • Computer Science Applications
  • Electrical and Electronic Engineering
  • Applied Mathematics

Fingerprint Dive into the research topics of 'Two are better than one: Adaptive sparse system identification using affine combination of two sparse adaptive filters'. Together they form a unique fingerprint.

  • Cite this